Pseudo-steppe with grasses hosts a variety of plants, vertebrates and invertebrates; this typically Mediterranean habitat is currently under the protection of the EU Habitat Directive as priority habitat type n° 6220, with several hosting sites being included in the Natura2000 network. Considering the stresses imposed by land use change and climate impacts, especially in southern Italy, monitoring the extent, structural and physiological parameters of this habitat over space and time is crucial to implement proper conservation and planning strategies. In this study, multispectral and SAR data have been linked to habitat extent and structural parameters in different sites in the Apulia region (southern Italy), where field calibration and validation data were collected in spring and summer 2024, to investigate the value of different remote sensing data in supporting habitat conservation within the Natura 2000 network.

In recent years, radar jamming systems have become a crucial research area in the field of remote sensing for defence and security tasks. In a multistatic Synthetic Aperture Radar (SAR), the presence of false targets created by a deceptive jammer is recognizable thanks to the presence of multiple receivers, whose positions might not be easily known by the jammer. In this configuration the bistatic SAR geometry can be designed so that false targets are kept out observed bistatic scene. In this paper, we propose a mathematical analysis that expresses the jammer’s delay parameter as a function of the bistatic angle, identifying conditions to develop strategies to consistently induce the appearance of false targets in the bistatic scene and an analysis on how to place the false targets in the same position in the different bistatic images.

SAR sensors play an important role for a wide range of remote sensing applications due to their all-weather capabilities and their independence from sunlight. However, SAR images are more difficult to interpret than electro-optical images, and are affected by speckle noise, a multiplicative phenomenon that is created by the coherent nature of the image formation process and that might severely hinder image interpretation. Thus, speckle filtering is of great importance and many different approaches have been suggested. While most classical speckle filters use the local statistics in a sliding window, more recently non-local filtering and Convolutional Neural Networks (CNNs) have been used. The approach suggested in this paper falls into the latter category. For supervised CNN approaches, many training chips are necessary to teach the network the task it should solve. In the case of speckle filtering, many speckled/unspeckled image pairs are necessary. In this paper, we suggest creating training data for a U-Net architecture using SAR simulation. The simulation results of several large 3d scenes are shown, which in turn are used for the training of the U-Net for speckle filtering. First results on a TerraSAR-X image are shown and the potential and limitations of the current state of the approach are discussed.

In this study, we investigate the synergic use of polarimetric Synthetic Aperture Radar (SAR) decompositions and electromagnetic models for soil moisture retrieval over corn fields. The Generalized Freeman-Durden decomposition (GFD) is applied to a time-series of L-band full-polarimetric SAOCOM-1A data collected during the 2019 to 2020 growing season over an agricultural area. The scattering mechanisms (i.e., surface, double-bounce, and volume) derived from the decomposition are compared with the ones simulated using the Tor Vergata electromagnetic model. The goal of the work is to evaluate the capabilities of the GFD to consistently assign each scattered power to the corresponding scattering mechanism, so that the sensitivity to soil moisture and vegetation can be highlighted. Results point out significative discrepancies, especially for the volume term, while a good agreement is found for the double-bounce contribution. Differences are further confirmed when a simple linear regression model is applied to retrieve soil moisture using the GFD scattered powers or the model powers.

This research explores the application of the Advanced Integral Equation Model (AIEM) for retrieving surface soil moisture in grassland ecosystems. The AIEM was utilized to analyze VV-polarized backscatter coefficients obtained from Sentinel-1, incorporating Sentinel-2-derived NDVI to address the challenge of separating soil and vegetation scattering contributions in grasslands. Calibration of the model was performed using field measurements from the Texas Soil Observation Network (TxSON), spanning from 2019 to 2021. The soil moisture estimates derived from the model were rigorously validated against ground-truth data from TxSON for the year 2023. To evaluate the accuracy and reliability of the retrieved soil moisture data, statistical analyses, including trend analysis, Sen’s slope estimation, and the Mann-Kendall test, were conducted. The results demonstrated that the model successfully captured soil moisture dynamics throughout the entire vegetation period. Although the modeled soil moisture values were marginally higher than the observed values, the overall trend between the modeled and observed data was consistently aligned. This study underscores the potential of AIEM, in conjunction with multi-sensor satellite data, for accurate soil moisture retrieval in grassland ecosystems. The findings provide valuable insights into soil-vegetation-atmosphere interactions and have significant implications for rangeland management, precision agriculture, and hydrological modeling in grassland environments.

This study aims at improving the spatial resolution of snow depth (SD) products derived from microwave satellite radiometers by proposing a disaggregation method based on X-band SAR data. The method has been developed and tested in the western part of Italian Alps, by involving Cosmo SkyMed (CSK) and AMSR-2 data. Machine learning methods play a twofold role in the proposed active/passive (A/P) implementation: the AMSR-2 data disaggregation process is indeed based on Artificial Neural Networks (ANN), while the SD retrieval using the disaggregated data is based on ANN and Random Forest (RF) algorithms. To assess the effectiveness of the proposed A/P technique, the SD retrievals have been compared with those obtained by estimating SD directly from CSK data. Taking advantage of the multifrequency information, the retrievals based on A/P method clearly outperformed those based on CSK data only: correlation increased from R=0.77 to R= 0.85 for the ANN based retrievals and from 0.76 to 0.86 for the RF based retrievals. The corresponding RMSE decreases from 34cm to 28cm and from 34cm to 27cm for ANN and RF, respectively, in a SD range between 0 and ≃220cm.

This study focuses on precipitation retrievals over oceans in tropical cyclones by means of neural networks using data from the new NASA TROPICS (Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats) mission. Accurate monitoring of tropical cyclones is a major concern for both the scientific community and emergency management services due to the severe damage they cause, especially when they fall on population centers and surrounding areas. The TROPICS constellation consists of four CubeSats carrying on-board passive microwave radiometers providing high revisit time measurements over the Tropics with the aim to study in detail the structure and evolution of TCs during their lifecycle. A NN architecture for precipitation retrieval is developed and trained with data from the GPM (Global Precipitation Measurement) constellation providing reference value of precipitation and it is tested on an independent dataset constituted by TROPICS observations. An automatic spatial-temporal collocation procedure between TROPICS brightness temperatures and IMERG (Integrated Multi-satellitE Retrievals for GPM) data is performed in order to set up the training dataset. In this study the tropical storm Ida is considered as test case, and the preliminary results obtained are promising showing a R² between modeled (NN precipitation outputs) and reference target (IMERG precipitation product) above 0.8.

In this work, we analyze the 2023 Nyamulagira volcanic crisis using remote sensing Synthetic Aperture Radar (SAR) data acquired from January to June 2023 by Sentinel-1 missions. We perform a coherence variations analysis to constrain any magma movements inside the crater and lava flows along the flanks of the volcano. Outcomes reveal periodic phases of high and low volcanic activity along the entire 2023 with a strong crisis occurring in May, with lava completely filling and overflowing the caldera.

Typically, an image formed using the backprojection algorithm is the coherent sum of every pulse’s contribution to every image pixel, accounting for the respective time delays and phase corrections. This allows for highly accurate image reconstruction. The modification proposed, differs in that the contributions of every pulse are concatenated to form a 3D radar data cube, instead of being coherently summed. This approach allows for the precise analysis of how the phase of individual target pixels change over time. In this work, the phase is utilized to accurately reconstruct the amplitude and frequency of a vibrating target. This method is demonstrated on both simulated data and compensated phase history data (CPHD) acquired by Capella Space.

We propose a tool that integrates spaceborne radar data from Sentinel-1 and multispectral data from Sentinel-2 satellites to detect fresh debris accumulation or rock exposure on alpine glaciers. The tool leverages the VH cross-polarized backscatter difference between Sentinel-1 radar images to pinpoint clusters of pixels exhibiting prominent changes over time. It then integrates the NDSI information from Sentinel-2 multispectral images to discriminate between potential causes of the observed backscatter variations, such as snow cover changes, rock exposure or debris accumulation. The correlation between time series of VH values further allows to distinguish between localized phenomena like landslides and extensive ones like glacial surface changes due to snow metamorphism or accumulation. The tool is implemented in Google Earth Engine (GEE) and leverages open-source libraries and datasets, making it readily adaptable to other glacial environments.

The development of remote sensing techniques and methods, such as GNSS Reflectometry (GNSS-R), has become a prominent technology in various applications and topics of interest today. GNSS-R is a bistatic radar application that implements remote sensing for Earth observation. This technique is pivotal in determining environmental and geophysical properties of the Earth’s water and ground surfaces, including ocean wind speeds, surface material permittivity, and water content. Several significant satellite missions, such as TechDemoSat-1 (TDS-1) by SSTL, NASA’s Cyclone Global Navigation Satellite System (CYGNSS), and the European Space Agency’s FSSCat, have been launched to pioneer and explore this technology from space. The GNSS Reflectometry & Occultation eXperiment Field Test Campaign (GNSS-ROX FTC) constitutes a series of ground- and air-based field test campaigns conducted in the context of developing a GNSS-R payload for the Seamless Radio Access networks for Internet of Space (SeRANIS) satellite mission. The inaugural field test (GNSS-ROX FTC 1) was carried out using an airborne hexa-copter drone to confirm hardware setup and demonstrate proof of concept. The subsequent field test (GNSS-ROX FTC 2) involved a ground-based GNSS-R measurement from a high-altitude vantage point overlooking two water bodies—Lake Walchen and Lake Kochel. Notable GPS signal reflections for both Left Hand Circularly Polarized (LHCP) and Right Hand Circularly Polarized (RHCP) were observed at the recording antenna at certain points. The objective of these measurements was to receive, geolocalize reflection points, and conduct a thorough analysis. The paper discusses the development of the second field test and analyzes the findings and methods of precise reflection point geopositioning, considering the realistic and challenging settings of the Earth’s surfaces.

In Synthetic Aperture Radar image data, control point acquisition poses a major challenge, as object signatures can vary strongly depending on the incidence and aspect angle. The definition of suitable control points is essential and requires investigation of their backscatter variation. Since the choice of such control points is determined by the requirement to find stable and permanent points, man-made structures such as lamp posts or traffic sign systems seem to be the most reliable. However, in many cases, it is difficult to identify such targets of opportunity in an observed scene. In particular, for interferometric measurements, artificial corner reflectors can be used and positioned to provide controlled coherent targets. To minimize the effort for such measurements, it is important that the corner reflectors are suitable for ascending and descending orbits and cover as many different azimuth angles as possible. In this study, height-adjustable quad-corner reflectors (half-octahedral structure), designed by IOSB, are assessed with regard to their suitability as position reference and control points in the context of a persistent scatterer measurements. In a measurement campaign variation in reflector alignment were simulated using a circular arrangement of a corner group. A total of 17 corners were used with two different reflector sizes and placed on two different background structures. One TerraSAR-X (TSX) image was acquired over this measurement setup in the highest-resolution staring spotlight mode, in which the subsequent analyses of radar backscatter were carried out. Additionally, the height adjustability of the IOSB corners was used in a persistent scatterer measurement campaign to create controlled height displacements.

In the last two decades the use of satellite remote sensing (RS) methods and technologies to study the ongoing processes on land surface and more specifically to obtain information about movements of the earth's surface due to geodynamic processes with different magnitudes (earthquakes, tectonic movements, landslides and collapse processes and etc.) has become ubiquitous due to more data available from operational synthetic aperture radars. The main driving force for this increase of the application of RS in studying the mentioned processes are the technological progress in the space industry, the creation of RS instruments for monitoring different objects from the Earth's surface with better spatial accuracy, improvement of the computational capabilities of modern computer systems as well as the increasingly and widespread development of innovative SAR data processing methods. One of the data sources for the thematic categories under the Copernicus program is the Sentinel-1 (S-1) satellite, which provides reliable SAR data regardless of atmospheric conditions. After their thematic processing by using the method of differential interferometry (DInSAR) and its improvements it is possible to obtain information about undelaying topography as well as the displacements that have occurred on the earth's surface. In order to increase the quality of this information for non-urbanized territories, it is necessary to deploy artificial, passive persistent reflectors (also known as corner reflectors - CRs) at well-known positions having verified backscattering characteristics. And since on the territory of Bulgaria, to the best knowledge of the authors, there are no available such passive reflectors as described above that are used to improve the reliability of the information obtained in SAR data processing from S-1 we designed, developed, and put into operation on a pilot basis a network of six reflectors. In this paper we present the setting-up process as well as first results after their deployment at selected site to study surface deformations caused by active fault structures.

In recent years, Multi-Temporal InSAR (MT-InSAR) techniques have become popular as an effective way of monitoring volcanic areas and the ground deformations caused by their activity. This work used MT-InSAR techniques to investigate Lipari, Salina, and Vulcano islands belonging to the Aeolian archipelago (southern Tyrrhenian Sea, Italy). In particular, three distinct approaches were applied and compared: PS, SBAS, and IPTA. These techniques were compared with in-situ measurements from the GNSS network managed by INGV-OE and private operators considering different metrics. The findings indicate that the combination of these three MT-InSAR techniques has both advantages and disadvantages for monitoring these complex areas.

High fidelity simulation of continuous correlated sea clutter with long-term space-time correlation characteristics has always been a challenge. A memoryless nonlinear transform (MNLT) based sea clutter intensity simulation followed by a continuous phase retrieval method based on alternating projections (AP) algorithm provides a kind of solution with promising performance. In this paper, a recursive algorithm is proposed which can be used to replace the fast Fourier transform (FFT) for long-term sea clutter phase retrieval under the constrain of the desired time-varying Doppler spectra. Simulation results based on the parameter estimation of Council for Scientific and Industrial Research (CSIR) Fynmeet radar data demonstrate that the proposed recursive algorithm generates complex sea clutter data with exact space-time correlation characteristics as specified, while with much less calculation.

This paper proposes a novel method for complete model-based decomposition of Pol-InSAR data. By decomposing the interferometric cross-coherence matrix, the approach separates scattering mechanisms (odd-bounce, double-bounce, and volume scattering) for each pixel. Unlike traditional methods using the polarimetric coherence matrix, this approach tackles the challenge of a non-Hermitian cross-coherence matrix, leading to non-real eigenvalues. A new approach is introduced to address this non-symmetry issue. The retrieved parameters include both magnitude and phase information, ultimately aiming to classify each pixel and determine its vertical position.

The synthetic aperture radar (SAR) offset tracking method is extensively employed to accurately measure significant surface displacements resulting from phenomena such as glacier melting, volcanic eruptions, and earthquakes, particularly when the interferometric phase lacks coherence. However, a trade-off exists between the resolution and accuracy of SAR offset tracking, determined by the selected kernel sizes. Hence, choosing optimal kernel sizes is crucial in the application of this method. In this study, we applied SAR offset tracking with a coarse-to-fine strategy, applying the kernel sizes coarsely and then fine. This approach allows for improved observational precision while maintaining resolution compared to general single-kernel offset tracking results. Applying this technique to SAR imagery from KOMPSAT-5, a South Korean X-band SAR satellite, enabled the precise observation of surface displacements caused by the melting of the Campbell Glacier in the East Antarctic and the 2023 Turkey-Syria earthquake. This marks the first instance of large-scale surface displacement observations using KOMPSAT-5 SAR imagery, affirming the effectiveness of the SAR offset tracking technique for precise land surface displacement observations with KOMPSAT-5 SAR imagery.

This study discusses the versatility of a method developed by the authors that uses pre- and post-disaster SAR data to accurately assess the building damaged areas during a large-scale earthquake. After reviewing the outline of the method, we report the results of applying it to the Noto Peninsula earthquake that occurred in January 2024 and evaluating the extent of damage at the municipal level. Three combinations of paired data acquired by L-band ALOS-2 PALSAR-2 and C-band Sentinel-1 C-SAR were used to calculate the coherence. Seven municipalities near the epicenter in Ishikawa Prefecture were targeted, and building damage rate, which indicate the ratio of completely destroyed buildings to the total number of buildings, were calculated from data published by the prefecture. The urban areas were then extracted from the coherence using the land cover, and average coherence in each municipality was compared with the damage rate. The result showed that although the coherence was greater or less depending on the combination, there was a significant negative correlation between the average coherence and the damage rate in all combinations. Our finding indicated that the proposed coherence-based method was effective in assessing damage at the municipal level.

This study presents an assessment of the displacements of the Earth's crust generated in the last ten years in the region of the Balkan Peninsula after recorded earthquakes. The Balkan Peninsula is one of the most seismic areas in Europe, since the beginning of the twentieth century several superficial destructive earthquakes of magnitude greater than 6.0 have been recorded. Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technique allowing to measure deformation with cm-precision over large areas. We are focused on the co-seismic displacements associated with earthquakes with over value 5.5 Mw happening on to the Balkan Peninsula. The main data source is SAR satellite images from Sentinel-1A and B-from ascending and descending orbits. The results were obtained applying the differential radar interferometry method (DInSAR) and compared with deformations of the Earth's surface registered by GNSS and other geophysics methods.